Multiple path planning for a group of mobile robot in a 2-D environment using genetic algorithms

Abstract

Considers the development and implementation of an approach using genetic algorithms for finding optimum paths for a group of mobile robots located at arbitrary starting positions to a given number of targets in a known multi-obstacle environment. The factors considered for finding the optimum paths for the group of mobile robots are the location and size of obstacles in the environment. The environment is first converted into a grid map. Each grid is assigned a weight value, which indicates the level of confidence for the robots to move on that grid. The grid map contains information about the positions of the robots, the targets, the obstacle locations and sizes. Two genetic algorithms modules have been developed to find the optimum paths for the mobile robots. The first genetic algorithm module takes information about the environment from the grid map and finds an obstacle-free optimum path for each mobile robot to each target. The second genetic algorithm module finds the best combination of mobile robots to move to a given number of targets.